Search Results (9)

This work presents an incremental zoom analog-to-digital converter (ADC) readout integrated circuit (IC) and field-programmable gate array (FPGA)-based digital calibration system for high-precision capacitive micro electro-mechanical system (MEMS) pressure sensors, aimed at enhancing pressure readout resolution, pressure measurement accuracy, and long-term output stability. In the readout IC, the zoom ADC employs coarse-fine quantization, achieving high readout accuracy while relaxing requirements on integrator output swing and front-end linearity. On the digital side, a sparrow-search-algorithm-optimized Gaussian process regression (SSA-GPR) model constructs a nonlinear pressure–temperature mapping with limited calibration samples, while a segmented aging-compensation scheme based on dual-channel periodic self-test dynamically corrects aging-induced offset and sensitivity drifts. Together, these three techniques form a complete signal-conditioning chain addressing weak capacitance variation readout, pressure–temperature coupling, and long-term drift. Measured results show 0.11 Pa_RMS output root mean square (RMS) noise under a 205.2 ms conversion time, 37.44 Pa mean absolute error over −40 °C to 85 °C and 30 kPa to 120 kPa, and ±30 Pa residual error after 85 °C/1000 h aging, confirming the effectiveness of the proposed readout IC and digital calibration system. Full article

Autonomous agricultural robots require accurate trajectory tracking to perform precision field operations such as seeding, fertilization, and pruning. Classical kinematic models fail to capture the nonlinear dynamics inherent to real field conditions, limiting the performance of model-based control strategies. This work proposes an optimal hybrid methodology integrating Takagi–Sugeno (T–S) fuzzy model identification and Pure Pursuit (PP) control within a Particle Swarm Optimization (PSO) framework for a simulated pruning tractor. Data-driven T–S fuzzy models for incremental displacements ${\mathcal{M}}_{\Delta x}$ and ${\mathcal{M}}_{\Delta y}$ are identified using Fuzzy C-Means and parameterized via PSO. These fuzzy models are embedded in a PP feedback control scheme with discrete-time PI velocity and PD steering controllers, whose four gains are tuned by a second PSO instance. The fuzzy models achieve identification Root-Mean-Square Errors (RMSEs) of 10.598 × 10⁻³ m and 8.125 × 10⁻³ m. Integrated into the control loop, the system yields a lateral RMSE of 6.6 × 10⁻³ m on the training path and generalizes effectively across twelve complex agricultural coverage trajectories, maintaining a lateral RMSE below 12 × 10⁻³ m and heading RMSE under 1 degree. This interpretable, fuzzy rule-based approach provides an accurate and replicable simulation baseline for future experimental implementation on physical platforms. Full article

The infrared satellite data have become an important source of assimilated data in numerical weather prediction (NWP) models. With the self-constructed assimilated module in the Weather Research and Forecasting model’s Data Assimilation (WRFDA) system, a set of cycling assimilation experiments is conducted to evaluate the added value of assimilating the Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water vapor channels clear-sky data on analyses and forecasts for “23 · 7” heavy rainfall. The results show a notable reduction (50~60%) in the root mean square error (RMSE) of observed and simulated brightness temperature after assimilating AGRI and the positive analysis increments in temperature and humidity fields, which are conducive to precipitation formation. Furthermore, changes in humidity analysis caused by AGRI assimilation propagate from the upper to lower levels with assimilation cycling. Compared to the benchmark experiment, the AGRI assimilation experiments produce higher humidity conditions and more pronounced ascending motion, resulting in more realistic rainfall predictions at both location and intensity with higher rainfall scores, especially with the two-step assimilation scheme. Moreover, based on the results from sensitivity experiments, it is proven that the addition of a new channel 11 can further improve humidity and enhance rainfall location and intensity predictions. Overall, the clear-sky assimilation of the FY-4B AGRI water vapor channel data brings notable improvements to “23 · 7” heavy rainfall prediction. Full article

Stochastic differential equations (SDEs) with multiple conserved quantities are ubiquitous in scientific fields, modeling systems from molecular dynamics to celestial mechanics. While geometric numerical integrators that preserve single invariants are well-established, constructing efficient and high-order numerical schemes for SDEs with multiple conserved quantities remains a challenge. Existing approaches often suffer from high computational costs or lack desirable numerical properties like symmetry. This paper introduces two novel classes of projection-based numerical methods tailored for SDEs with multiple conserved quantities. The first method projects the increments of an underlying numerical scheme onto a discrete tangent space, ensuring all invariants are preserved by construction. The second method leverages a local coordinates approach, transforming the SDE onto the manifold defined by the invariants, solving it numerically, and then projecting back, guaranteeing the solution evolves on the correct manifold. We prove that both methods inherit the mean-square convergence order of their underlying schemes. Furthermore, we propose a simplified strategy that reduces computational expense by redefining the multiple invariants into a single one, offering a practical trade-off between exact preservation and efficiency. Numerical experiments confirm the theoretical findings and demonstrate the superior efficiency and structure-preserving capabilities of our methods. Full article

Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means. Full article

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches. Full article

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R²) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m² and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R² by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems. Full article

With the continuous increment of photovoltaic (PV) energy connection into a power grid, the accuracy of control parameters of PV power generation systems becomes the key to the stable operation of the power grid. At present, parameter identification based on an intelligent algorithm is a common means to obtain control parameters. However, most of the data used for identification are simulation data and the identified parameters are difficult to use in practical engineering. Therefore, aiming at the acquisition of low voltage ride through (LVRT) control parameters of PV unit, a method of identification of LVRT parameters of the PV unit is proposed, which combines sensitivity analysis with field measurement. In this paper, the test scheme of the required data is put forward through sensitivity analysis of the identified parameters, and the intelligent algorithm is used to identify the low voltage traverse parameters of the data. Finally, the optimal value is extracted from the identification results and substituted into the model. The accuracy of the parameter identification results is verified by calculating the error between the output of the model and the real operation data. The method considers the errors caused by different power levels of the inverters with highly accurate and consistent identification results, which is applicable to practical engineering calculation. Full article

The present paper aims at computational simulations of particle reinforced Metal Matrix Composites as well as parts and specimens made thereof. An incremental Mori-Tanaka approach with isotropization of the matrix tangent operator is adopted. It is extended to account for large strains by means of co-rotational Cauchy stresses and logarithmic strains and is implemented into Finite Element Method software as constitutive material law. Periodic unit cell predictions in the finite strain regime are used to verify the analytical approach with respect to non-proportional loading scenarios and assumptions concerning finite strain localization. The response of parts made of Metal Matrix Composites is predicted by a multiscale approach based on these two micromechanical methods. Results for the mesoscopic stress and strain fields as well as the microfields are presented to demonstrate to capabilities of the developed methods. Full article